Capnographic analysis for disease classification

Abstract

Existing methods for extracting diagnostic information from carbon dioxide in the exhaled breath are qualitative, through visual inspection, and therefore imprecise. In this thesis, we quantify the CO₂ waveform, or capnogram, in order to discriminate among various lung disorders. Quantitative analyses of the capnogram are conducted by extracting several physiological waveform features and performing classification by discriminant analysis with voting. Our classification methods are tested in distinguishing between records from subjects with normal lung function and patients with cardiorespiratory disease. In a second step, we discriminate between capnograms from patients with obstructive lung disease (chronic obstructive pulmonary disease) and those with restrictive lung disease (congestive heart failure). Our results demonstrate the diagnostic potential of capnography.